Roberta De Vito is an assistant professor in the Department of Biostatistics and the Data Science Initiative at Brown University with a strong passion for developing innovative statistical models and tools for health problems.
Her work aims broadly on developing and advancing methods for the analysis of high-dimensional, heterogeneous data sets to identify and understand the association and the impact of nutrition and genomic data on diseases. In this big picture, she uses Bayesian approaches and machine learning techniques to investigate issues, such as cancer prevention and detection, and depression on children and teenagers.
Dr. De Vito received her B.S. in Statistics from University La Sapienza in Rome, Italy, and her Ph.D. in Statistics from the University of Padua in Italy. She was a former visiting Ph.D. Student at Harvard T.H. Chan School of Public Health under the supervision of Giovanni Parmigiani. She did her postdoctoral training with Barbara Engelhardt at Princeton University.